In general, there has been a considerable amount of emphasis and/or research to accurately and efficiently monitor, control and analyze complex industrial systems. The disciplines of industrial engineering and systems engineering were, in part, formed as a result of specific requirements and/or knowledge required to address the above mentioned concerns.
To that end, one major field that has developed within the systems engineering discipline is the field of control engineering. The control engineering field seeks to find better ways to control, for example, physical industrial systems. The well known concept of feedback control provides the capability for accurate control of industrial systems by feeding back the control error from the desired or reference control point. The feedback of the error is commonly called negative feedback.
Control engineering has developed over the years many different types of controllers that predict future system behavior and provide inputs/controls to prevent unwanted behavior and to encourage desired behavior. One notoriously well known example is the PID (proportional-integral-derivative) controller. The PID controller essentially predicts future system behavior based on immediate past behavior of the system.
The proportional control variable is used to correct for any offset errors from the system characteristics. The integral control variable is used to correct errors that accumulate overtime. Finally, the derivative variable is used to correct for rapid changes in system behavior. The resulting PID controller is extremely robust and able to provide good control of the system.
Other type of controllers include, for example, adaptive controllers that are used to control systems that change over time. By anticipating changes in a system over time, the adaptive controller is able to predict the changed system performance, thereby resulting in finer or more accurate system control.
Another field of interest that has developed as a result of the systems engineering and industrial engineering fields is the field of signal transmission. For example, sensors in a factory monitor factory performance. The sensor transmits data back to a central computer for processing, analysis, and the like. The challenge in the signal transmission field is to transmit the least amount of data while still maintaining essentially the same quality of information needed to effectively monitor the factory performance. As a result, many different techniques have arisen that attempt to transmit less data. These techniques, generally called data compression techniques, attempt to compress the amount of data that must be transmitted to the processor.
Data compression techniques all center on new computer processes that permit the processor to interpolate or estimate the data that has been compressed and not transmitted. For example, one technique involves only the transmitting of data if, for example, a sensor detects a change in previous conditions beyond a predetermined threshold. In this scenario, the sensor will not transmit information that is redundant and that merely reconfirms existing recorded conditions of the system being sensed or monitored. Thus, data compression techniques center upon or emphasize the need to convey only significant information to monitor, control or analyze system performance in an effective manner.
Another technique that has also developed as a result of the systems engineering field is the field of systems simulation. In this field, a systems engineer models a physical system mathematically. The system engineer subsequently exercises or runs simulations on the system model to learn how the system might react under varying conditions or circumstances. The challenge in the systems simulation field is to accurately model a system in a manner which is not mathematically complex, or else the simulation will not be able to be efficiently executed by computer. Accordingly, many different types of algorithms have been developed that attempt to model system behavior in an efficient manner which are practical for execution by a computer.
Accordingly, the prior art attempts have generally focused on developing better models, better controls or more efficient ways to transmit data for monitoring system performance. However, these prior art attempts have generally not focused on better to ways to prepare system models for execution. These prior art attempts have also not considered or concentrated on more efficient ways of combining various different system characteristics to prepare the model for execution or implementation.
These prior art attempts have also not concentrated on more efficient methods of monitoring the progress of model execution based on general model characteristics. That is, the prior has not catered or customized display/monitor techniques that are particularly efficient for computer implementation. Further, the prior art techniques have not capitalized on the unique aspects of object oriented technology that are particularly well suited to provide these more efficient system models and execution of same.
We have determined, therefore, that it is desirable to design better ways to prepare system models for execution. We have also realized that it is desirable to design more efficient ways of combining various different system characteristics to prepare the model for execution or implementation.
We have also realized that more efficient methods of monitoring the progress of model execution based on general model characteristics are needed. In this connection, we have defined a graphic model as a combination of static and dynamic objects, animation of real-time variables, input controls (GISMOs-discussed below), and control actions. Static objects can consist of elements as simple as squares, or as sophisticated as AUTOCAD drawings or bitmaps. Input controls within a graphic model can be anything from numeric or text boxes, to buttons, sliders and menus. It is therefore desirable to design customized display/monitor techniques that are particularly efficient for computer implementation.
Further, we have determined that the unique aspects of object oriented techniques are particularly well suited to provide these more efficient system models and execution of same.